It is advantageous in seismic data processing and interpretation to reduce a seismic data volume to its internal reflection-based surfaces or horizons. Collectively, these surfaces form the skeleton of the seismic volume. Many methods have been described to extract or track one horizon or surface at a time through a volume of seismic data. Most of these methods create surfaces that eventually overlap themselves. Thus, the same surface may have multiple depths (or reflection times) associated with the same spatial position. Some methods prevent multi-valued surfaces by discarding all but one value per location. Typically, they store only the first one encountered during the execution of the process and simply do not record later ones. Moreover, if multiple surfaces are tracked, one surface may overlay another surface at one location, while the opposite relationship occurs at another location. Collectively, these situations may be termed topologically inconsistent. The published approaches to date, some of which are summarized below, largely ignore topological consistency.
In “The Binary Consistency Checking Scheme and Its Applications to Seismic Horizon Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 439-447 (1989), Cheng and Lu describe a method to extract the seismic skeleton from two dimensional data. Problems introduced by the third dimensions are neither discussed nor resolved. The procedure uses an iterative approach where strong horizons are tracked initially, while weaker ones are tracked in later iterations. At any iteration, the tracking is confined to areas delineated by horizons already tracked in earlier iterations. Tracking is preformed by correlating multiple neighboring traces simultaneously. Combining the two approaches allows incorporation of the geologic fabric into the results. This method is also described in “An Iterative Approach to Seismic Skeletonization,” Lu and Cheng, Geophysics 55, 1312-1320 (1990).
In “Seismic Skeletonization: A New Approach to Interpretation of Seismic Reflection Data,” Journal of Geophysical Research—Solid Earth 102, 8427-8445 (1997), L1, Vasudevan, and Cook describe the utility of using the seismic skeleton for the interpretation of seismic data. The seismic skeleton is two dimensional, and when a horizon splits, the decision regarding which branch to follow is not geologically motivated. Instead, the method attempts to correlate events across three neighboring traces in such a way that dip changes are minimized. The method includes only iterative growing of horizons.
Further, “Adaptation of Seismic Skeletonization for Other Geoscience Applications,” Vasudevan, Eaton, and Cook, Geophysical Journal International 162, 975-993 (2005), is a continuation of the earlier work, realizing that skeletonization has geoscience applications beyond seismic processing and interpretation.
In “Branch And Bound Search For Automatic Linking Process Of Seismic Horizons,” Huang, Pattern Recognition 23, 657-667 (1990), Huang discloses a two dimensional method of horizon growth allowing horizons to cross and penetrate each other, which violates the stratigraphic paradigm that geologic strata do not cross. The method reveals only the generation of horizons by picking events, peaks for example, building a tree of all potential linkages between these events, and then selecting the ones which yield the most linear horizons. Branches of the linage tree are chosen to minimize a cost function of horizon nonlinearity.
“How To Create And Use 3D Wheeler Transformed Seismic Volumes,” de Groot, de Bruin, and Hemstra, SEG 2006 discloses an interpretation method that interpolates horizons with sub-sampling resolution by following the local dips and strikes, organizes these horizons in sequential order, and visualizes these horizons or attributes thereon in a depositional domain by flattening of the horizons or attribute volumes along the horizons. Specifically, the algorithm requires the input of major horizons which need to be picked with an alternative method, such as manual picking. Within an interval bracketed by major horizons, minor horizons are interpolated either parallel to the top or bottom horizons, linearly interpolated in between, or following the local dip and strike orientations estimated from seismic attributes. By construction, the interpolated minor horizons are not crossing through each other.
In a paper submitted for the 70th EAGE (European Association of Geoscientists and Engineers) Conference and Exhibition, Rome, Italy, Jun. 9-12, 2008, and available for download at www.earthdoc.org beginning May 26, 2008, entitled “An Approach of Seismic Interpretation Based on Cognitive Vision,” Verney et al. disclose a method for geology-based interpretation of seismic data by using artificial intelligence tools based on “cognitive vision.” First order reflector continuity is detected using voxel connectivity in the seismic data. Then, a visual characterization step is performed. For example, chronological relationships are established based on whether a reflector lies above or below another. Finally, geological horizons are identified from the reflectors by fusing all nodes that (a) share similar visual attributes (amplitude, thickness, dip), and (b) are located at similar distances from at least one other reflector. The result is a set of chronologically ordered horizons.
U.S. Pat. No. 7,024,021, “Method for Performing Stratigraphically-Based Seed Detection in a 3-D Seismic Data Volume,” to Dunn and Czernuszenko, discloses a three-dimensional geobody picker and analyzer. In this patent, a few select geobodies are picked, which may include geobodies having attribute values within a specified range or geobodies adjacent to certain attribute values. During picking, the geobodies are analyzed using a map view criteria to detect and eliminate self-overlapping geobodies, and yielding composite geobodies instead. The composite geobodies satisfy at least the topological condition of no self overlaps, but the boundaries between geobodies are determined by the order in which the voxels are detected.
In “System and Method for Displaying Seismic Horizons with Attributes” (PCT Patent Application Publication No. WO 2007046107), James discloses a seismic autopicker that generates single valued horizons and often takes the correct branch when horizons split. The interpreter initializes the method by manually selecting one or multiple seed points within the seismic data volume. The algorithm uses the seed points to pick a set of secondary points from neighboring traces which are then treated as new seed points, and the procedure repeats. Secondary picks that led to self overlap are rejected, but topological consistency with other horizons is not revealed. The algorithm is basically based on controlled marching.
U.S. Pat. No. 7,257,488 to Cacas (“Method of Sedimentologic Interpretation by Estimation of Various Chronological Scenarios of Sedimentary Layers Deposition”) discloses a method of organizing seismic and geologic horizons into a hierarchy using the above/below relationships to facilitate their stratigraphic interpretation. The method automatically extracts pertinent information for sedimentologic interpretation from seismic data by using estimations of realistic chronological scenarios of sedimentary layers deposition. The algorithm begins by thresholding the seismic data and using morphological thinning to create individual horizons. If multiple horizons intersect, then the most linear pair is combined, while the others are explicitly disconnected. The method then iteratively estimates a first and a second chronological scenario of the deposition of sedimentary layers, assuming respectively that each reflector settles at the earliest and at the latest possible moment during the sedimentary depositional process. Starting with reference horizons, the algorithm basically enumerates the horizons upwards and downwards to establish relative orders. An interpretation of these two chronological scenarios is eventually carried out so as to reconstruct the depositional conditions of the sedimentary layers.
The differences in the relative orders are used to estimate the scenario uncertainty.
GB Patent No. 2,444,167 to Cacas (“Method for Stratigraphic Interpretation of Seismic Images”) discloses a method for stratigraphic interpretation of a seismic image for determination of the sedimentary history of the subsurface. The method involves automatically tracking events creating at least one horizon, selecting horizons with similar seismic attributes extracted from a window at or near the horizons, and flattening the seismic volume along the selected horizons.
U.S. Pat. No. 7,248,539 to Borgos (“Extrema Classification”) discloses a method of horizon patch formation and merging by common membership in clusters of waveforms and patch properties. The method picks horizons by extracting, e.g., all peaks, but correlates them by clustering of waveforms. Picks belonging to the same cluster are used to define horizons patches which are merged into larger horizons by properties such as cluster indices, position, or seismic attributes. Specifically, the method defines with sub-sample precision the positions of seismic horizons through an extrema representation of a 3D seismic input volume. For each extrema, it derives coefficients that represent the shape of the seismic waveform in the vicinity of the extrema positions and sorts the extrema positions into groups that have similar waveform shapes by using unsupervised or supervised classification of these coefficients. It then extracts surface primitives as surface segments that are both spatially continuous along the extrema of the seismic volume and continuous in class index in the classification volume. By filtering on properties, such as class index, position, attribute values, etc. attached to each patch, a set of patches can be combined into a final horizon interpretation. Three primary applications of the surface primitives are revealed: combining surface primitives into complete horizons for interpretations; defining closed volumes within the seismic volume as the closure of vertically arranged surface primitives; or estimating fault displacement based on the surface primitives.
Monsen et al. (“Geologic-process-controlled interpretation based on 3D Wheeler diagram generation,” SEG 2007) extended U.S. Pat. No. 7,248,539 to Borgos by extracting above/below relationships for the patches and used these relationships to derive a relative order of patches which satisfies these constraints by application of a topological sort. Flattened horizons are then positioned in this relative order to allow interpretation in the depositional Wheeler domain. The SEG abstract is the basis for U.S. Patent Application Publication No. US 2008/0140319, published on Jun. 12, 2008.
GB Patent No. 2,375,448 to Pedersen (“Extracting Features from an Image by Automatic Selection of Pixels Associated with a Desired Feature, Pedersen”) discloses a method to construct surfaces, such as horizons and faults from a few select seed points. The method interpolates between the seed points and extrapolates away from the seed points by generating many paths which slowly converge to lines (in two dimensions) or surfaces (in three dimensions). The method is based on the way ants leave the colony to forage for food. Initially, their paths are nearly random, but each ant leaves a trail of pheromones. Ants follow each other's scent, and over time, short successful paths emerge. This strategy was adapted to horizon tracking where success is defined by the coherency of the seismic data along the path. For fault picking, success appears to be defined by the incoherency along the path. Over time, individual segments grow, and some may merge to form larger surfaces. In a follow-up step, segments are connected depending on their orientations and projected trajectories.
U.S. Pat. No. 5,570,106 (“Method and Apparatus for Creating Horizons from 3-D Seismic Data”) to Viswanathan discloses a method for computer-assisted horizon picking by allowing the user to delete partial horizons and use the remaining horizon as seed points for automatic picking.
U.S. Pat. No. 5,537,365 (“Apparatus and Method for Evaluation of Picking Horizons in 3-D Seismic Data”) to Sitoh discloses a method to evaluate the quality of horizon picks by applying different picking strategies and parameter to allow crosschecking of results.
U.S. Pat. No. 6,850,845 to Stark discloses a method to convert seismic data to a domain of relative geologic time of deposition. The method is based on the unwrapping of seismic instantaneous phase data.
U.S. Pat. No. 6,771,800 (“Method of Chrono-Stratigraphic Interpretation of A Seismic Cross Section Or Block”) to Keskes et al. discloses a method to transform seismic data into the depositional or chronostratigraphic domain. They construct virtual reflectors, discretize the seismic section or volume, count the number of virtual reflectors in each pixel or voxel, and renormalizing this histogram. By doing this procedure for every trace, they create a section or volume where each horizontal slice approximates a horizon indicating a geologic layer deposited at one time. This section or volume is then used to transform the data into the depositional or chronostratigraphic domain. However, the reference does not disclose the creation of surfaces, nor breaking or merging of surfaces, nor topology or topological consistency.
What is needed is a method that generates topologically consistent reflection horizons from seismic (or attribute) data or any geophysical data, preferably one that generates multiple horizons simultaneously. The present invention fulfills this need.